A Deep Learning-Based Tumor Classifier Directly Using MS Raw Data.

Since the launch of Chinese Human Proteome Project (CNHPP) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), large-scale mass spectrometry (MS) based proteomic profiling of different kinds of human tumor samples have provided huge amount of valuable data for both basic and clinical researchers. Accurate prediction for tumor and non-tumor samples, as well as the tumor types has become a key step for biological and medical research, such as biomarker discovery, diagnosis and monitoring of diseases. The traditional MS-based classification strategy mainly depends on the identification and quantification results of MS data, which has some inherent limitations, such as the low identification rate of MS data. Here, we proposed a deep learning-based tumor classifier directly using MS raw data, which is independent of the identification and quantification results of MS data. We firstly detected and extracted the potential precursors with intensities and retention times from MS data as input. Then, we trained a deep learning-based classifier, which can accurately distinguish between the tumor and non-tumor samples. Finally, we demonstrated that the deep learning-based classifier has a good performance compared with other machine learning methods and may help researchers to find the potential biomarkers which are likely to be missed by the traditional strategy. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.